{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy import stats\n",
    "import sklearn.preprocessing \n",
    "from datetime import datetime \n",
    "from sklearn.linear_model import RidgeCV \n",
    "import numpy as np\n",
    "import sklearn.metrics\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "df_day = pd.read_csv('/Users/panyang/Desktop/day.csv',index_col='instant')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of              dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "instant                                                               \n",
       "1        2011-01-01       1   0     1        0        6           0   \n",
       "2        2011-01-02       1   0     1        0        0           0   \n",
       "3        2011-01-03       1   0     1        0        1           1   \n",
       "4        2011-01-04       1   0     1        0        2           1   \n",
       "5        2011-01-05       1   0     1        0        3           1   \n",
       "6        2011-01-06       1   0     1        0        4           1   \n",
       "7        2011-01-07       1   0     1        0        5           1   \n",
       "8        2011-01-08       1   0     1        0        6           0   \n",
       "9        2011-01-09       1   0     1        0        0           0   \n",
       "10       2011-01-10       1   0     1        0        1           1   \n",
       "11       2011-01-11       1   0     1        0        2           1   \n",
       "12       2011-01-12       1   0     1        0        3           1   \n",
       "13       2011-01-13       1   0     1        0        4           1   \n",
       "14       2011-01-14       1   0     1        0        5           1   \n",
       "15       2011-01-15       1   0     1        0        6           0   \n",
       "16       2011-01-16       1   0     1        0        0           0   \n",
       "17       2011-01-17       1   0     1        1        1           0   \n",
       "18       2011-01-18       1   0     1        0        2           1   \n",
       "19       2011-01-19       1   0     1        0        3           1   \n",
       "20       2011-01-20       1   0     1        0        4           1   \n",
       "21       2011-01-21       1   0     1        0        5           1   \n",
       "22       2011-01-22       1   0     1        0        6           0   \n",
       "23       2011-01-23       1   0     1        0        0           0   \n",
       "24       2011-01-24       1   0     1        0        1           1   \n",
       "25       2011-01-25       1   0     1        0        2           1   \n",
       "26       2011-01-26       1   0     1        0        3           1   \n",
       "27       2011-01-27       1   0     1        0        4           1   \n",
       "28       2011-01-28       1   0     1        0        5           1   \n",
       "29       2011-01-29       1   0     1        0        6           0   \n",
       "30       2011-01-30       1   0     1        0        0           0   \n",
       "...             ...     ...  ..   ...      ...      ...         ...   \n",
       "702      2012-12-02       4   1    12        0        0           0   \n",
       "703      2012-12-03       4   1    12        0        1           1   \n",
       "704      2012-12-04       4   1    12        0        2           1   \n",
       "705      2012-12-05       4   1    12        0        3           1   \n",
       "706      2012-12-06       4   1    12        0        4           1   \n",
       "707      2012-12-07       4   1    12        0        5           1   \n",
       "708      2012-12-08       4   1    12        0        6           0   \n",
       "709      2012-12-09       4   1    12        0        0           0   \n",
       "710      2012-12-10       4   1    12        0        1           1   \n",
       "711      2012-12-11       4   1    12        0        2           1   \n",
       "712      2012-12-12       4   1    12        0        3           1   \n",
       "713      2012-12-13       4   1    12        0        4           1   \n",
       "714      2012-12-14       4   1    12        0        5           1   \n",
       "715      2012-12-15       4   1    12        0        6           0   \n",
       "716      2012-12-16       4   1    12        0        0           0   \n",
       "717      2012-12-17       4   1    12        0        1           1   \n",
       "718      2012-12-18       4   1    12        0        2           1   \n",
       "719      2012-12-19       4   1    12        0        3           1   \n",
       "720      2012-12-20       4   1    12        0        4           1   \n",
       "721      2012-12-21       1   1    12        0        5           1   \n",
       "722      2012-12-22       1   1    12        0        6           0   \n",
       "723      2012-12-23       1   1    12        0        0           0   \n",
       "724      2012-12-24       1   1    12        0        1           1   \n",
       "725      2012-12-25       1   1    12        1        2           0   \n",
       "726      2012-12-26       1   1    12        0        3           1   \n",
       "727      2012-12-27       1   1    12        0        4           1   \n",
       "728      2012-12-28       1   1    12        0        5           1   \n",
       "729      2012-12-29       1   1    12        0        6           0   \n",
       "730      2012-12-30       1   1    12        0        0           0   \n",
       "731      2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "         weathersit      temp     atemp       hum  windspeed  casual  \\\n",
       "instant                                                                \n",
       "1                 2  0.344167  0.363625  0.805833   0.160446     331   \n",
       "2                 2  0.363478  0.353739  0.696087   0.248539     131   \n",
       "3                 1  0.196364  0.189405  0.437273   0.248309     120   \n",
       "4                 1  0.200000  0.212122  0.590435   0.160296     108   \n",
       "5                 1  0.226957  0.229270  0.436957   0.186900      82   \n",
       "6                 1  0.204348  0.233209  0.518261   0.089565      88   \n",
       "7                 2  0.196522  0.208839  0.498696   0.168726     148   \n",
       "8                 2  0.165000  0.162254  0.535833   0.266804      68   \n",
       "9                 1  0.138333  0.116175  0.434167   0.361950      54   \n",
       "10                1  0.150833  0.150888  0.482917   0.223267      41   \n",
       "11                2  0.169091  0.191464  0.686364   0.122132      43   \n",
       "12                1  0.172727  0.160473  0.599545   0.304627      25   \n",
       "13                1  0.165000  0.150883  0.470417   0.301000      38   \n",
       "14                1  0.160870  0.188413  0.537826   0.126548      54   \n",
       "15                2  0.233333  0.248112  0.498750   0.157963     222   \n",
       "16                1  0.231667  0.234217  0.483750   0.188433     251   \n",
       "17                2  0.175833  0.176771  0.537500   0.194017     117   \n",
       "18                2  0.216667  0.232333  0.861667   0.146775       9   \n",
       "19                2  0.292174  0.298422  0.741739   0.208317      78   \n",
       "20                2  0.261667  0.255050  0.538333   0.195904      83   \n",
       "21                1  0.177500  0.157833  0.457083   0.353242      75   \n",
       "22                1  0.059130  0.079070  0.400000   0.171970      93   \n",
       "23                1  0.096522  0.098839  0.436522   0.246600     150   \n",
       "24                1  0.097391  0.117930  0.491739   0.158330      86   \n",
       "25                2  0.223478  0.234526  0.616957   0.129796     186   \n",
       "26                3  0.217500  0.203600  0.862500   0.293850      34   \n",
       "27                1  0.195000  0.219700  0.687500   0.113837      15   \n",
       "28                2  0.203478  0.223317  0.793043   0.123300      38   \n",
       "29                1  0.196522  0.212126  0.651739   0.145365     123   \n",
       "30                1  0.216522  0.250322  0.722174   0.073983     140   \n",
       "...             ...       ...       ...       ...        ...     ...   \n",
       "702               2  0.347500  0.359208  0.823333   0.124379     892   \n",
       "703               1  0.452500  0.455796  0.767500   0.082721     555   \n",
       "704               1  0.475833  0.469054  0.733750   0.174129     551   \n",
       "705               1  0.438333  0.428012  0.485000   0.324021     331   \n",
       "706               1  0.255833  0.258204  0.508750   0.174754     340   \n",
       "707               2  0.320833  0.321958  0.764167   0.130600     349   \n",
       "708               2  0.381667  0.389508  0.911250   0.101379    1153   \n",
       "709               2  0.384167  0.390146  0.905417   0.157975     441   \n",
       "710               2  0.435833  0.435575  0.925000   0.190308     329   \n",
       "711               2  0.353333  0.338363  0.596667   0.296037     282   \n",
       "712               2  0.297500  0.297338  0.538333   0.162937     310   \n",
       "713               1  0.295833  0.294188  0.485833   0.174129     425   \n",
       "714               1  0.281667  0.294192  0.642917   0.131229     429   \n",
       "715               1  0.324167  0.338383  0.650417   0.106350     767   \n",
       "716               2  0.362500  0.369938  0.838750   0.100742     538   \n",
       "717               2  0.393333  0.401500  0.907083   0.098258     212   \n",
       "718               1  0.410833  0.409708  0.666250   0.221404     433   \n",
       "719               1  0.332500  0.342162  0.625417   0.184092     333   \n",
       "720               2  0.330000  0.335217  0.667917   0.132463     314   \n",
       "721               2  0.326667  0.301767  0.556667   0.374383     221   \n",
       "722               1  0.265833  0.236113  0.441250   0.407346     205   \n",
       "723               1  0.245833  0.259471  0.515417   0.133083     408   \n",
       "724               2  0.231304  0.258900  0.791304   0.077230     174   \n",
       "725               2  0.291304  0.294465  0.734783   0.168726     440   \n",
       "726               3  0.243333  0.220333  0.823333   0.316546       9   \n",
       "727               2  0.254167  0.226642  0.652917   0.350133     247   \n",
       "728               2  0.253333  0.255046  0.590000   0.155471     644   \n",
       "729               2  0.253333  0.242400  0.752917   0.124383     159   \n",
       "730               1  0.255833  0.231700  0.483333   0.350754     364   \n",
       "731               2  0.215833  0.223487  0.577500   0.154846     439   \n",
       "\n",
       "         registered   cnt  \n",
       "instant                    \n",
       "1               654   985  \n",
       "2               670   801  \n",
       "3              1229  1349  \n",
       "4              1454  1562  \n",
       "5              1518  1600  \n",
       "6              1518  1606  \n",
       "7              1362  1510  \n",
       "8               891   959  \n",
       "9               768   822  \n",
       "10             1280  1321  \n",
       "11             1220  1263  \n",
       "12             1137  1162  \n",
       "13             1368  1406  \n",
       "14             1367  1421  \n",
       "15             1026  1248  \n",
       "16              953  1204  \n",
       "17              883  1000  \n",
       "18              674   683  \n",
       "19             1572  1650  \n",
       "20             1844  1927  \n",
       "21             1468  1543  \n",
       "22              888   981  \n",
       "23              836   986  \n",
       "24             1330  1416  \n",
       "25             1799  1985  \n",
       "26              472   506  \n",
       "27              416   431  \n",
       "28             1129  1167  \n",
       "29              975  1098  \n",
       "30              956  1096  \n",
       "...             ...   ...  \n",
       "702            3757  4649  \n",
       "703            5679  6234  \n",
       "704            6055  6606  \n",
       "705            5398  5729  \n",
       "706            5035  5375  \n",
       "707            4659  5008  \n",
       "708            4429  5582  \n",
       "709            2787  3228  \n",
       "710            4841  5170  \n",
       "711            5219  5501  \n",
       "712            5009  5319  \n",
       "713            5107  5532  \n",
       "714            5182  5611  \n",
       "715            4280  5047  \n",
       "716            3248  3786  \n",
       "717            4373  4585  \n",
       "718            5124  5557  \n",
       "719            4934  5267  \n",
       "720            3814  4128  \n",
       "721            3402  3623  \n",
       "722            1544  1749  \n",
       "723            1379  1787  \n",
       "724             746   920  \n",
       "725             573  1013  \n",
       "726             432   441  \n",
       "727            1867  2114  \n",
       "728            2451  3095  \n",
       "729            1182  1341  \n",
       "730            1432  1796  \n",
       "731            2290  2729  \n",
       "\n",
       "[731 rows x 15 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "strptime() argument 1 must be str, not Timestamp",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-9c99ca75ddd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#训练数据和测试数据分割（请将2012年的数据作为测试数据）；（20分）\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%Y-%m-%d\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtrain_data_2011\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'2012'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest_data_2012\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m'2012'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/panyang/anaconda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   2549\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2550\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2551\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-9c99ca75ddd4>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#训练数据和测试数据分割（请将2012年的数据作为测试数据）；（20分）\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"%Y-%m-%d\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtrain_data_2011\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'2012'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtest_data_2012\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_day\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dteday'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;34m'2012'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: strptime() argument 1 must be str, not Timestamp"
     ]
    }
   ],
   "source": [
    "#训练数据和测试数据分割（请将2012年的数据作为测试数据）；（20分）\n",
    "df_day['dteday'] = df_day['dteday'].apply(lambda a : datetime.strptime(a, \"%Y-%m-%d\"))\n",
    "train_data_2011 = df_day[df_day['dteday'] < '2012']  \n",
    "test_data_2012 = df_day[df_day['dteday'] >= '2012']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
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       "      <th>cnt</th>\n",
       "    </tr>\n",
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       "      <th>instant</th>\n",
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       "      <td>2011-01-01</td>\n",
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       "      <td>654</td>\n",
       "      <td>985</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-03</td>\n",
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       "      <td>2011-01-04</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
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       "      <th>5</th>\n",
       "      <td>2011-01-05</td>\n",
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       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "            dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "instant                                                              \n",
       "1       2011-01-01       1   0     1        0        6           0   \n",
       "2       2011-01-02       1   0     1        0        0           0   \n",
       "3       2011-01-03       1   0     1        0        1           1   \n",
       "4       2011-01-04       1   0     1        0        2           1   \n",
       "5       2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "         weathersit      temp     atemp       hum  windspeed  casual  \\\n",
       "instant                                                                \n",
       "1                 2  0.344167  0.363625  0.805833   0.160446     331   \n",
       "2                 2  0.363478  0.353739  0.696087   0.248539     131   \n",
       "3                 1  0.196364  0.189405  0.437273   0.248309     120   \n",
       "4                 1  0.200000  0.212122  0.590435   0.160296     108   \n",
       "5                 1  0.226957  0.229270  0.436957   0.186900      82   \n",
       "\n",
       "         registered   cnt  \n",
       "instant                    \n",
       "1               654   985  \n",
       "2               670   801  \n",
       "3              1229  1349  \n",
       "4              1454  1562  \n",
       "5              1518  1600  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_2011.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
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       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>677.402740</td>\n",
       "      <td>2728.358904</td>\n",
       "      <td>3405.761644</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>556.269121</td>\n",
       "      <td>1060.110413</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>3632.000000</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3065.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>6043.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season     yr        mnth     holiday     weekday  workingday  \\\n",
       "count  365.000000  365.0  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     2.498630    0.0    6.526027    0.027397    3.008219    0.684932   \n",
       "std      1.110946    0.0    3.452584    0.163462    2.006155    0.465181   \n",
       "min      1.000000    0.0    1.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    0.0    4.000000    0.000000    1.000000    0.000000   \n",
       "50%      3.000000    0.0    7.000000    0.000000    3.000000    1.000000   \n",
       "75%      3.000000    0.0   10.000000    0.000000    5.000000    1.000000   \n",
       "max      4.000000    0.0   12.000000    1.000000    6.000000    1.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   365.000000   365.000000   365.000000  \n",
       "mean    677.402740  2728.358904  3405.761644  \n",
       "std     556.269121  1060.110413  1378.753666  \n",
       "min       9.000000   416.000000   431.000000  \n",
       "25%     222.000000  1730.000000  2132.000000  \n",
       "50%     614.000000  2915.000000  3740.000000  \n",
       "75%     871.000000  3632.000000  4586.000000  \n",
       "max    3065.000000  4614.000000  6043.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据统计量\n",
    "train_data_2011.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#数值特征和类别特征\n",
    "data_columns_suti = ['temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered', 'cnt']\n",
    "data_columns_biaoceng = ['dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Td/jrq6++ij179sBkurpbcXBwEC+++CJ2794Nk8mEDRs24J577sHx48cRiUSw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\niywozjejKNeMwlwT8mxGmI3alDj2xg3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a170ca5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1Q5YV0eVQEjCE6bKcG/QCACoYwkRxUWTPQSSqYMjNqZtswBCmOQtHZPQP+WC3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\nircOdUOjVuErtyzC2uVloss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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a171b7cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PPvggFEVBRUUFsrOzr7lQokTVZXdDVhTkpHMomq7NwtJMNLQP4o2PGrG8PAfJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20218080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oJliyaW534wAA4PSafJmTUDaorciFUiGgrsWJaIw79BBNhyWbxsKRGPY3D8Fh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\nERFJ5P8D5ZA8wvp84o4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2029ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xWAwajWbQbV6vFxaLZch9VCrVFa+1Wq1444030NbWhi9/+ctoamqCVqtFbm4u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\nJwIAtUqFeTMyce+8PBh0arx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ccQcEQUBOTg6i0Si6u7ulbsKgrrbinlzV1dXB7/fjK1/5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\nIlIwBjkREZGCMciJiIgU7P8HR4sbORWu2sAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20346198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nWjlxvg1LhJFv3TqTyHBJ7lqzDQ+0kylrQ4okeCEmoKWjn33HG1BVuHtZ2k1N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\nhRBCCBf0v2pKce8RtJ52AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a203cf828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WTW/AkM2J1i6L3OWQgjDgiShgXd96l3852FuVljB2z/74bX5E3sCAJ6KAJEkSzjYGdut9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\nxoAPcn1DI3j2T7UQBOBH316ASEOI3CU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2045d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数值特征分布\n",
    "for i in data_columns_suti:\n",
    "    plt.figure()\n",
    "    sns.distplot(train_data_2011[i], kde=True, rug=False, bins=10) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/panyang/anaconda/lib/python3.6/site-packages/seaborn/categorical.py:1428: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a209337b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20913ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a211ef5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a211a7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20f49d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20d1b940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21375940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a213a4160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#类别特征分布\n",
    "for i in data_columns_biaoceng:\n",
    "    plt.figure()\n",
    "    sns.countplot(train_data_2011[i]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/panyang/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "/Users/panyang/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#'cnt'一维5聚类 \n",
    "def cluster_cnt(data, k):\n",
    "    from sklearn.cluster import KMeans\n",
    "    clf = KMeans(n_clusters=k) \n",
    "    s =clf.fit(data.reshape(len(data),1)) \n",
    "    return s.labels_ , s.cluster_centers_\n",
    "train_data_2011['label'], centers = cluster_cnt(train_data_2011['cnt'], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#卡方检验 选取有关联的类别特征\n",
    "def soukan(data, key, columns):   \n",
    "    cross_syukei = {}\n",
    "    kai2 = {}\n",
    "        #ｶｲ2乗      \n",
    "    for i in columns:\n",
    "        if i != key:\n",
    "            b=pd.crosstab(data[key],data[i],margins=True)\n",
    "            cross_syukei[i]=b\n",
    "            \n",
    "            #ｶｲ2乗値を計算            \n",
    "            b=b.drop('All',axis=0)\n",
    "            b=b.drop('All',axis=1)\n",
    "            #b.plot.bar()\n",
    "            kai2_1, kai2_2, _, _=stats.chi2_contingency(b)\n",
    "            if kai2_2 <= 0.05:\n",
    "                kai2[i]=[kai2_1, kai2_2]\n",
    "            else:\n",
    "                pass                \n",
    "    return  kai2 ,cross_syukei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "显示与cnt有关联的类别特征: {'season': [248.73450096875047, 2.5119405947515049e-46], 'mnth': [445.52617088398534, 7.8408829129495156e-68], 'workingday': [10.949899332413365, 0.027132884617688934], 'weathersit': [49.324341776934325, 5.5094632199890068e-08]}\n"
     ]
    }
   ],
   "source": [
    "kai_soukan, cross_syukei = soukan(data= train_data_2011, key='label', columns= data_columns_biaoceng)\n",
    "print('显示与cnt有关联的类别特征:',kai_soukan) #显示与cnt有关联的类别特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a214ba828>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RQmvWrNEf/vAHLV68uML0M2fOaOXKlXrvvfeUmJio2bNnX3WeBDUAwNaMy7h9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\n9L2qbac7+ysAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a214ba860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#共分散\n",
    "corr = train_data_2011[data_columns_suti].corr()\n",
    "sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp          0.771214\n",
       "atemp         0.775294\n",
       "casual        0.708359\n",
       "registered    0.928880\n",
       "cnt           1.000000\n",
       "Name: cnt, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#与cnt线性相关的数值特称\n",
    "corr['cnt'][corr['cnt']>0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xTIMJhdZUHnajk5vCBbjwCtlpIuxnQNJCCF9iEgSOlzBpMEqIknY+Hkt1m49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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20f9d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vvntMFyuWAW64qAo3XFjdq1gZebXE0mgUK43pFQUIRUW0jnHjBQkWQRBjBiEu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2178f630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L119/IxdddEnSz/+FL3wGp9MFQFFRAd/85nd4+OF/obGxEYDa2hoWLVrMv//7j4b+YiYp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a217328d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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W+yPQ0Q1+vJOEJYQQnSRbLTW3hfjja3upbUocuphpN3HDpXMpzcvANYJl6/3h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\nMAuG4zz77k68bjtLTqpOdziHRBJWFupbst7UHubR17ZxwvTRvPJhg1V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a201bcf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#与cnt线性有关的特征的散点图\n",
    "for i in corr['cnt'][corr['cnt']>0.5].index:\n",
    "    if i != 'cnt':\n",
    "        sns.jointplot(i, 'cnt', data=train_data_2011,kind=\"reg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#选去与cnt相关的数值和类别特征\n",
    "input_data_column = [i for i in kai_soukan.keys()]#选取卡方检验后置信区间小于0.05的特征\n",
    "input_data_column_2 = list(corr[np.abs(corr['cnt']) > 0.3].index) #与cnt的Person相关系数大于0.3的数值特征 \n",
    "input_data_column_final = input_data_column + input_data_column_2\n",
    "input_data_column_final.remove('cnt') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型输入特征： ['season', 'mnth', 'workingday', 'weathersit', 'temp', 'atemp', 'casual', 'registered']\n"
     ]
    }
   ],
   "source": [
    "print('模型输入特征：',input_data_column_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/panyang/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/panyang/anaconda/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n",
      "/Users/panyang/anaconda/lib/python3.6/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "#归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "scale =  MinMaxScaler()\n",
    "train_data_2011[input_data_column_2] = scale.fit_transform(train_data_2011[input_data_column_2])\n",
    "test_data_2012[input_data_column_2] = scale.transform(test_data_2012[input_data_column_2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.076898</td>\n",
       "      <td>0.307527</td>\n",
       "      <td>0.270848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115019</td>\n",
       "      <td>0.061963</td>\n",
       "      <td>0.441250</td>\n",
       "      <td>0.365671</td>\n",
       "      <td>0.026178</td>\n",
       "      <td>0.412339</td>\n",
       "      <td>0.321632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.061225</td>\n",
       "      <td>0.052856</td>\n",
       "      <td>0.414583</td>\n",
       "      <td>0.184700</td>\n",
       "      <td>0.028141</td>\n",
       "      <td>0.442354</td>\n",
       "      <td>0.345153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.261637</td>\n",
       "      <td>0.261664</td>\n",
       "      <td>0.524167</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>0.042866</td>\n",
       "      <td>0.646975</td>\n",
       "      <td>0.506237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "instant                                                              \n",
       "366     2012-01-01       1   1     1        0        0           0   \n",
       "367     2012-01-02       1   1     1        1        1           0   \n",
       "368     2012-01-03       1   1     1        0        2           1   \n",
       "369     2012-01-04       1   1     1        0        3           1   \n",
       "370     2012-01-05       1   1     1        0        4           1   \n",
       "\n",
       "         weathersit      temp     atemp       hum  windspeed    casual  \\\n",
       "instant                                                                  \n",
       "366               1  0.393488  0.389264  0.692500   0.192167  0.221531   \n",
       "367               1  0.270763  0.227394  0.381304   0.329665  0.076898   \n",
       "368               1  0.115019  0.061963  0.441250   0.365671  0.026178   \n",
       "369               2  0.061225  0.052856  0.414583   0.184700  0.028141   \n",
       "370               1  0.261637  0.261664  0.524167   0.129987  0.042866   \n",
       "\n",
       "         registered       cnt  \n",
       "instant                        \n",
       "366        0.283945  0.331967  \n",
       "367        0.307527  0.270848  \n",
       "368        0.412339  0.321632  \n",
       "369        0.442354  0.345153  \n",
       "370        0.646975  0.506237  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_2012.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instant</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.360789</td>\n",
       "      <td>0.373517</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>0.105366</td>\n",
       "      <td>0.056694</td>\n",
       "      <td>0.098717</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.385232</td>\n",
       "      <td>0.360541</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>0.039921</td>\n",
       "      <td>0.060505</td>\n",
       "      <td>0.065930</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.173705</td>\n",
       "      <td>0.144830</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>0.036322</td>\n",
       "      <td>0.193664</td>\n",
       "      <td>0.163578</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.178308</td>\n",
       "      <td>0.174649</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>0.032395</td>\n",
       "      <td>0.247261</td>\n",
       "      <td>0.201532</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.212429</td>\n",
       "      <td>0.197158</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>0.023887</td>\n",
       "      <td>0.262506</td>\n",
       "      <td>0.208304</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "instant                                                              \n",
       "1       2011-01-01       1   0     1        0        6           0   \n",
       "2       2011-01-02       1   0     1        0        0           0   \n",
       "3       2011-01-03       1   0     1        0        1           1   \n",
       "4       2011-01-04       1   0     1        0        2           1   \n",
       "5       2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "         weathersit      temp     atemp       hum  windspeed    casual  \\\n",
       "instant                                                                  \n",
       "1                 2  0.360789  0.373517  0.805833   0.160446  0.105366   \n",
       "2                 2  0.385232  0.360541  0.696087   0.248539  0.039921   \n",
       "3                 1  0.173705  0.144830  0.437273   0.248309  0.036322   \n",
       "4                 1  0.178308  0.174649  0.590435   0.160296  0.032395   \n",
       "5                 1  0.212429  0.197158  0.436957   0.186900  0.023887   \n",
       "\n",
       "         registered       cnt  label  \n",
       "instant                               \n",
       "1          0.056694  0.098717      4  \n",
       "2          0.060505  0.065930      4  \n",
       "3          0.193664  0.163578      4  \n",
       "4          0.247261  0.201532      4  \n",
       "5          0.262506  0.208304      4  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data_2011.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Rigde L2 model \n",
    "Reg = RidgeCV(store_cv_values=True) #选取预设alpha值（0.1 ，1， 10）进行预测\n",
    "Reg.fit(train_data_2011[input_data_column_final],train_data_2011['cnt'])\n",
    "y_hat = Reg.predict(test_data_2012[input_data_column_final]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10000000000000001"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reg.alpha_ #显示当前最佳的alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha_set = np.linspace(0.001, 0.15, 750) #显示在最佳的alpha附近继续寻找最佳alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RidgeCV(alphas=array([ 0.001 ,  0.0012, ...,  0.1498,  0.15  ]), cv=None,\n",
       "    fit_intercept=True, gcv_mode=None, normalize=False, scoring=None,\n",
       "    store_cv_values=True)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reg.set_params(alphas=alpha_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Reg.fit(train_data_2011[input_data_column_final],train_data_2011['cnt'])\n",
    "y_hat = Reg.predict(test_data_2012[input_data_column_final])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_alpha: 0.001\n"
     ]
    }
   ],
   "source": [
    "#最適な\n",
    "print('best_alpha:', Reg.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a202cceb8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iMFl5xfh6ujL98f706ubj6FiilZD1cIQQAOSdL+PdTw9RXF5L/55+/O7Rfni5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\nCykcIYQQdvH/Acgo2QxjJfahAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a21495630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示各个alpha下的cv值\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_xlabel('alpha')\n",
    "ax.set_ylabel('cv_values_mean')\n",
    "plt.plot(alpha_set ,Reg.cv_values_.mean(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_train: 0.99999999401\n"
     ]
    }
   ],
   "source": [
    "#训练集的R2\n",
    "print('r2_train:',sklearn.metrics.r2_score(train_data_2011['cnt'], Reg.predict(train_data_2011[input_data_column_final])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_test: 0.999999963441\n"
     ]
    }
   ],
   "source": [
    "#测试集のR2\n",
    "sklearn.metrics.mean_absolute_error(test_data_2012['cnt'], y_hat)\n",
    "print('r2_test:', sklearn.metrics.r2_score(test_data_2012['cnt'], y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2059ea58>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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75z//OQoLC/Gd73wH7e3tePjhh+H1egOPO51OmEwmGAwGOJ3OcduNRuO47WP7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\ndb09/DWSNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a202f99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#测试集的残差分布\n",
    "res = test_data_2012['cnt'] - y_hat\n",
    "sns.distplot(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a202f9898>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Dnj17sHfvXvz617/Grl274HK58OKLLyI3NxcvvPACbrrpJuzevRsAsG3bNuzc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\n+m807noAAAAFSURBVP9zDRQs68y50gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2093eef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#训练集的残差分布\n",
    "res_train = train_data_2011['cnt'] - Reg.predict(train_data_2011[input_data_column_final])\n",
    "sns.distplot(res_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Lasso L1 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Las = LassoCV()\n",
    "Las.fit(train_data_2011[input_data_column_final],train_data_2011['cnt'])\n",
    "y_hat = Las.predict(test_data_2012[input_data_column_final])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a206cd668>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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um2B3tOLP247i5fe+RU2j1dvRiEYsloKbTO3DFxoVD66GkkwmYPak0Vi7bApSxoTi8Ol6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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a202579e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示各个alpha下的mse值\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_xlabel('alpha')\n",
    "ax.set_ylabel('mse')\n",
    "plt.plot(Las.alphas_ ,Las.mse_path_.mean(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_alpha: 0.000376095628928\n"
     ]
    }
   ],
   "source": [
    "print('best_alpha:', Las.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r2_train: 0.999910520227\n",
      "r2_test: 0.99975819948\n"
     ]
    }
   ],
   "source": [
    "print('r2_train:',sklearn.metrics.r2_score(train_data_2011['cnt'], Las.predict(train_data_2011[input_data_column_final])))\n",
    "print('r2_test:', sklearn.metrics.r2_score(test_data_2012['cnt'], y_hat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a20c46ac8>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/yudVBQEozjZjbkk6Us1T3/P0xqEmdPd78emlBTAbNFP+fNGQlaqHSa/G+RYuNEHxi4FM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\nJAMMZCIiIhn4P/9IujoO8XDBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2023d940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#テストデータ残差分布\n",
    "Las_res_test = test_data_2012['cnt'] - y_hat\n",
    "sns.distplot(Las_res_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a206a2ac8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CFlybBQB4e+eZkO9fFEX8afsJfH6wFhmJZtw8MwPx0UP/8qJRK3H9tJ5QPlPbgbN1XOyE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\nmYiISAb+P95tOkM/xFbNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20449be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#トレーンデータ残差分布\n",
    "Las_res_train = train_data_2011['cnt'] - Las.predict(train_data_2011[input_data_column_final])\n",
    "sns.distplot(Las_res_train)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
